Advancing eHMI for powered wheelchairs beyond safety and communication: a pilot study on enriching social interaction through a co-design approach
Bibliographic record
Abstract
Background Enhancing safety and communication while minimizing unwanted attention is key for wheelchair external Human-Machine Interfaces (eHMIs). This study aims to introduce an interface to enhance eHMIs for powered wheelchairs, improve external communication, and enhance positive social interactions in challenging urban situations. Methods A co-design approach was adopted, centering wheelchair users (WUs) in a two-step methodology. First, data were collected through a qualitative survey to define criteria, which informed themes for focus group discussions. These themes guided the ideation process. Eighteen participants, including WUs and experts in cognitive psychology, physiotherapy, and design, were involved. Concepts developed in ideation sessions were analyzed using the Analytic Hierarchy Process. A prototype was then developed to be assessed by both WUs and pedestrians through a structured questionnaire. Results According to the analysis, four themes were identified: I. Streamlined Information in Interaction , II. User-Centric Safety Feedback , III. Harmonious and Minimalist Interaction Design , and IV. Effortless Integration and Production . Regarding these themes, a table with design suggestions and implications was introduced. Ultimately, five interface concepts were proposed, with Concept 2, ‘WheelSafe Illumina’ (41.3%), and Concept 1, ‘WheelGlow Assist’ (28.1%) emerging as top priorities, both featuring a shell structure. Concept 2 was developed for prototyping. The feedback from the experiences of both WUs and pedestrians indicate that the proposed eHMI may enhance perceived communication and safety without drawing negative attention. Conclusion Integrating eHMI into a shell structure improves functional communication while also minimizing unwanted attention toward WUs—an often-overlooked issue in previous research that our co-design approach identified and effectively addressed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".